整体特征通道识别的自适应孪生网络跟踪算法
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宋鹏,杨德东,李畅,郭畅
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An adaptive siamese network tracking algorithm based on global feature channel recognition
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Peng SONG,De-dong YANG,Chang LI,Chang GUO
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表 3 10种跟踪算法在OTB上11种属性的成功率 |
Tab.3 Success rate of ten tracking algorithms on eleven attributes of OTB |
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算法 | 光照变化 | 面内旋转 | 低分辨率 | 遮挡 | 面外旋转 | 出视野 | 尺度变化 | 快速移动 | 背景干扰 | 运动模糊 | 形变 | CFNET | 0.551 | 0.572 | 0.576 | 0.542 | 0.547 | 0.423 | 0.552 | 0.558 | 0.565 | 0.514 | 0.510 | SiamFC | 0.574 | 0.557 | 0.592 | 0.547 | 0.558 | 0.506 | 0.556 | 0.568 | 0.523 | 0.550 | 0.510 | SiamTri | 0.585 | 0.580 | 0.634 | 0.554 | 0.563 | 0.543 | 0.567 | 0.585 | 0.542 | 0.567 | 0.504 | LMCF | 0.601 | 0.543 | 0.450 | 0.554 | 0.553 | 0.539 | 0.519 | 0.551 | 0.606 | 0.561 | 0.525 | DSiamM | 0.608 | 0.599 | 0.606 | 0.583 | 0.599 | 0.509 | 0.576 | 0.579 | 0.589 | 0.562 | 0.544 | Staple | 0.596 | 0.552 | 0.418 | 0.545 | 0.531 | 0.481 | 0.518 | 0.537 | 0.574 | 0.546 | 0.552 | ECO-HC | 0.615 | 0.567 | 0.562 | 0.605 | 0.594 | 0.549 | 0.599 | 0.614 | 0.618 | 0.616 | 0.601 | DeepSRDCF | 0.624 | 0.589 | 0.475 | 0.603 | 0.607 | 0.553 | 0.607 | 0.628 | 0.627 | 0.642 | 0.567 | SiamDW | 0.656 | 0.611 | 0.607 | 0.598 | 0.615 | 0.588 | 0.625 | 0.627 | 0.596 | 0.659 | 0.608 | 本研究算法 | 0.666 | 0.633 | 0.585 | 0.618 | 0.642 | 0.582 | 0.636 | 0.648 | 0.636 | 0.669 | 0.638 |
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